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首页> 外文期刊>Reliability Engineering & System Safety >Dynamic artificial neural network-based reliability considering operational context of assets.
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Dynamic artificial neural network-based reliability considering operational context of assets.

机译：考虑到资产操作环境的基于动态人工神经网络的可靠性。

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摘要

Assets reliability is a key issue to consider in the maintenance management policy and given its importance several estimation methods and models have been proposed within the reliability engineering discipline. However, these models involve certain assumptions which are the source of different uncertainties inherent to the estimations. An important source of uncertainty is the operational context in which the assets operate and how it affects the different failures. Therefore, this paper contributes to the reduction of the uncertainty coming from the operational context with the proposal of a novel method and its validation through a case study. The proposed model specifically addresses changes in the operational context by implementing dynamic capabilities in a new conception of the Proportional Hazards Model. It also allows to model interactions among working environment variables as well as hidden phenomena thanks to the integration within the model of artificial neural network methods.
机译：资产可靠性是维护管理策略中要考虑的关键问题，鉴于其重要性，已经在可靠性工程学科内提出了几种估算方法和模型。但是，这些模型涉及某些假设，这些假设是估计所固有的不同不确定性的来源。不确定性的重要来源是资产的运行环境以及资产如何影响不同的故障。因此，本文提出了一种新颖的方法，并通过案例研究对其进行了验证，从而有助于降低操作环境中的不确定性。提议的模型通过在比例危害模型的新概念中实现动态功能来专门解决操作环境中的变化。由于在人工神经网络方法模型中的集成，它还允许对工作环境变量以及隐藏现象之间的交互进行建模。

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